Journal articles on the topic 'High Dynamic Range images (HDR)'
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Lu, Jiajun, Fangyan Dong, and Kaoru Hirota. "Gradient-Related Non-Photorealistic Rendering for High Dynamic Range Images." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 4 (July 20, 2013): 628–36. http://dx.doi.org/10.20965/jaciii.2013.p0628.
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A non-photorealistic rendering (NPR) method based on elements, usually strokes, is proposed for rendering high dynamic range (HDR) images to mimic the visual perception of human artists and designers. It enables strokes generated in the rendering process to be placed accurately on account of improvements in computing gradient values especially in regions having particularly high or low luminance. Experimental results using a designed pattern show that angles of gradient values obtained from HDR images have a reduction in averaged error of up to 57.5% in comparison to that of conventional digital images. A partial experiment on incorporating HDR images into other NPR styles, such as dithering, shows the wide compatibility of HDR images in providing source information for NPR processes.
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Dev, Soumyabrata, Florian M. Savoy, Yee Hui Lee, and Stefan Winkler. "High-dynamic-range imaging for cloud segmentation." Atmospheric Measurement Techniques 11, no. 4 (April 11, 2018): 2041–49. http://dx.doi.org/10.5194/amt-11-2041-2018.
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Abstract. Sky–cloud images obtained from ground-based sky cameras are usually captured using a fisheye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is overexposed, and the regions near the horizon are underexposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg – an effective method for cloud segmentation using high-dynamic-range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.
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Choi, Sungil, Jaehoon Cho, Wonil Song, Jihwan Choe, Jisung Yoo, and Kwanghoon Sohn. "Pyramid Inter-Attention for High Dynamic Range Imaging." Sensors 20, no. 18 (September 7, 2020): 5102. http://dx.doi.org/10.3390/s20185102.
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This paper proposes a novel approach to high-dynamic-range (HDR) imaging of dynamic scenes to eliminate ghosting artifacts in HDR images when in the presence of severe misalignment (large object or camera motion) in input low-dynamic-range (LDR) images. Recent non-flow-based methods suffer from ghosting artifacts in the presence of large object motion. Flow-based methods face the same issue since their optical flow algorithms yield huge alignment errors. To eliminate ghosting artifacts, we propose a simple yet effective alignment network for solving the misalignment. The proposed pyramid inter-attention module (PIAM) performs alignment of LDR features by leveraging inter-attention maps. Additionally, to boost the representation of aligned features in the merging process, we propose a dual excitation block (DEB) that recalibrates each feature both spatially and channel-wise. Exhaustive experimental results demonstrate the effectiveness of the proposed PIAM and DEB, achieving state-of-the-art performance in terms of producing ghost-free HDR images.
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Ledda, Patrick. "Product Review: High Dynamic Range Displays." Presence: Teleoperators and Virtual Environments 16, no. 1 (February 1, 2007): 119–22. http://dx.doi.org/10.1162/pres.16.1.119.
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In the natural world, the human eye is confronted with a wide range of colors and luminances. A surface lit by moonlight might have a luminance level of around 10−3 cd/m2, while surfaces lit during a sunny day could reach values larger than 105 cd/m2. A good quality CRT (cathode ray tube) or LCD (liquid crystal display) monitor is only able to achieve a maximum luminance of around 200 to 300 cd/m2 and a contrast ratio of not more than two orders of magnitude. In this context the contrast ratio or dynamic range is defined as the ratio of the highest to the lowest luminance. We call high dynamic range (HDR) images, those images (or scenes) in which the contrast ratio is larger than what a display can reproduce. In practice, any scene that contains some sort of light source and shadows is HDR. The main problem with HDR images is that they cannot be displayed, therefore although methods to create them do exist (by taking multiple photographs at different exposure times or using computer graphics 3D software for example) it is not possible to see both bright and dark areas simultaneously. (See Figure 1.) There is data that suggests that our eyes can see detail at any given adaptation level within a contrast of 10,000:1 between the brightest and darkest regions of a scene. Therefore an ideal display should be able to reproduce this range. In this review, we present two high dynamic range displays developed by Brightside Technologies (formerly Sunnybrook Technologies) which are capable, for the first time, of linearly displaying high contrast images. These displays are of great use for both researchers in the vision/graphics/VR/medical fields as well as professionals in the VFX/gaming/architectural industry.
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Shaya, Omar, Pengpeng Yang, Rongrong Ni, Yao Zhao, and Alessandro Piva. "A New Dataset for Source Identification of High Dynamic Range Images." Sensors 18, no. 11 (November 6, 2018): 3801. http://dx.doi.org/10.3390/s18113801.
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Digital source identification is one of the most important problems in the field of multimedia forensics. While Standard Dynamic Range (SDR) images are commonly analyzed, High Dynamic Range (HDR) images are a less common research subject, which leaves space for further analysis. In this paper, we present a novel database of HDR and SDR images captured in different conditions, including various capturing motions, scenes and devices. As a possible application of this dataset, the performance of the well-known reference pattern noise-based source identification algorithm was tested on both kinds of images. Results have shown difficulties in source identification conducted on HDR images, due to their complexity and wider dynamic range. It is concluded that capturing conditions and devices themselves can have an impact on source identification, thus leaving space for more research in this field.
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Ahirwal, Ramratan, Yogesh Singh Rajput, and Dr Yogendra Kumar Jain. "Ghost-Free High Dynamic Range Imaging Using Histogram Separation and Edge Preserving Denoising." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 3 (January 7, 2014): 3329–37. http://dx.doi.org/10.24297/ijct.v12i3.3242.
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In this paper, we introduce a ghost-free High Dynamic Range imaging algorithm for obtaining ghost-free high dynamicrange (HDR) images. The multiple image fusion based HDR method work only on condition that there is no movement ofcamera and object when capturing multiple, differently exposed low dynamic range (LDR) images. The proposed algorithmmakes three LDR images from a single input image to remove such an unrealistic condition. For this purpose a histogramseparation method is proposed in the algorithm for generating three LDR images by stretching each separated histogram.An edge-preserving denoising technique is also proposed in the algorithm to suppress the noise that is amplified in thestretching process. In the proposed algorithm final HDR image free from ghost artifacts in dynamic environment because itself-generates three LDR images from a single input image. Therefore, the proposed algorithm can be use in mobilephone camera and a consumer compact camera to provide the ghost artifacts free HDR images in the form of either inbuiltor post-processing software application.
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Li, Jie, Hai Wen Wang, and Xi Xi He. "Creation Method of High Dynamic Range Image Based on Three-Color Camera." Applied Mechanics and Materials 731 (January 2015): 193–96. http://dx.doi.org/10.4028/www.scientific.net/amm.731.193.
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The current HDR (High-Dynamic Range) images gets expensive display devices with low dynamic range of equipment problems, research objectives are presented methods for using ordinary camera fetching and displaying high dynamic range images. General three-color camera’s use is to obtain 3 different exposures of the same scene images, and binary image pyramid, followed by low-level image panning and rotation registration step by step, using HDR Darkroom Photomatix software obtains high dynamic range images ,tone mapping and detail enhancement, using Photoshop software to fine-tune to get the final high-dynamic range images. Visual evaluation and instrumental measurements shows the synthesis of high dynamic range images can increase reflects the brightness of the scene, details and colour information, application and promotion of the value of the method.
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Gracheva, I. A., and A. V. Kopylov. "TONE COMPRESSION ALGORITHM FOR HIGH DYNAMIC RANGE MEDICAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 87–95. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-87-2019.
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<p><strong>Abstract.</strong> We propose here an HDR compression method for medical images based on a windowing operator, an adaptive tone mapping operator, and the probabilistic normal-gamma model. First, we use the windowing operator based on a structural fidelity measure for optimal visualization of the input HDR medical image. Then, we transform the windowed image to the logarithm domain and split it into base and detail layers with the help of the probabilistic normal-gamma model. Base and detail layers are used to make the tone map with help the adaptive tone mapping operator. Finally, the tone mapping result is the LDR image. The proposed method has comparable quality and low computation time compared to other tone mapping operators.</p>
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See, Zi Siang, Lizbeth Goodman, Craig Hight, Mohd Shahrizal Sunar, Arindam Dey, Yen Kaow Ng, and Mark Billinghurst. "Creating high fidelity 360° virtual reality with high dynamic range spherical panorama images." Virtual Creativity 9, no. 1 (December 1, 2019): 73–109. http://dx.doi.org/10.1386/vcr_00006_1.
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Abstract This research explores the development of a novel method and apparatus for creating spherical panoramas enhanced with high dynamic range (HDR) for high fidelity Virtual Reality 360 degree (VR360) user experiences. A VR360 interactive panorama presentation using spherical panoramas can provide virtual interactivity and wider viewing coverage; with three degrees of freedom, users can look around in multiple directions within the VR360 experiences, gaining the sense of being in control of their own engagement. This degree of freedom is facilitated by the use of mobile displays or head-mount-devices. However, in terms of image reproduction, the exposure range can be a major difficulty in reproducing a high contrast real-world scene. Imaging variables caused by difficulties and obstacles can occur during the production process of spherical panorama facilitated with HDR. This may result in inaccurate image reproduction for location-based subjects, which will in turn result in a poor VR360 user experience. In this article we describe a HDR spherical panorama reproduction approach (workflow and best practice) which can shorten the production processes, and reduce imaging variables, and technical obstacles and issues to a minimum. This leads to improved photographic image reproduction with fewer visual abnormalities for VR360 experiences, which can be adaptable into a wide range of interactive design applications. We describe the process in detail and also report on a user study that shows the proposed approach creates images which viewers prefer, on the whole, to those created using more complicated HDR methods, or to those created without the use of HDR at all.
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Xie, Shundao, Wenfang Wu, Rongjun Chen, and Hong-Zhou Tan. "Reduced-Dimensional Capture of High-Dynamic Range Images with Compressive Sensing." Discrete Dynamics in Nature and Society 2020 (April 27, 2020): 1–13. http://dx.doi.org/10.1155/2020/6703528.
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The range of light illumination in real scenes is very large, and ordinary cameras can only record a small part of this range, which is far lower than the range of human eyes’ perception of light. High-dynamic range (HDR) imaging technology that has appeared in recent years can record a wider range of illumination than the perceptual range of the human eye. However, the current mainstream HDR imaging technology is to capture multiple low-dynamic range (LDR) images of the same scene with different exposures and then merge them into one HDR image, which greatly increases the amount of data captured. The advent of single-pixel cameras (compressive imaging system) has proved the feasibility of obtaining and restoring image data based on compressive sensing. Therefore, this paper proposes a method for reduced-dimensional capture of high dynamic range images with compressive sensing, which includes algorithms for front end (capturing) and back end (processing). At the front end, the K-SVD dictionary is used to compressive sensing the input multiple-exposure image sequence, thereby reducing the amount of data transmitted to the back end. At the back end, the Orthogonal Matching Pursuit (OMP) algorithm is used to reconstruct the input multiple-exposure image sequence. A low-rank PatchMatch algorithm is proposed to merge the reconstructed image sequence to obtain an HDR image. Simulation results show that, under the premise of reducing the complexity of the front-end equipment and the amount of communication data between the front end and the back end, the overall system achieves a good balance between the amount of calculation and the quality of the HDR image obtained.
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Tran, Van Luan, and Huei-Yung Lin. "Extending and Matching a High Dynamic Range Image from a Single Image." Sensors 20, no. 14 (July 16, 2020): 3950. http://dx.doi.org/10.3390/s20143950.
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Extending the dynamic range can present much richer contrasts and physical information from the traditional low dynamic range (LDR) images. To tackle this, we propose a method to generate a high dynamic range image from a single LDR image. In addition, a technique for the matching between the histogram of a high dynamic range (HDR) image and the original image is introduced. To evaluate the results, we utilize the dynamic range for independent image quality assessment. It recognizes the difference in subtle brightness, which is a significant role in the assessment of novel lighting, rendering, and imaging algorithms. The results show that the picture quality is improved, and the contrast is adjusted. The performance comparison with other methods is carried out using the predicted visibility (HDR-VDP-2). Compared to the results obtained from other techniques, our extended HDR images can present a wider dynamic range with a large difference between light and dark areas.
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Khan, Ishtiaq Rasool, and Susanto Rahardja. "Unified implementation of global high dynamic range image tone-mapping algorithms." Mathematical Biosciences and Engineering 19, no. 5 (2022): 4643–56. http://dx.doi.org/10.3934/mbe.2022215.
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<abstract> <p>High dynamic range (HDR) images and video require tone-mapping for display on low dynamic range (LDR) screens. Many tone-mapping operators have been proposed to convert HDR content to LDR, but almost each has a different implementation structure and requires a different execution time. We propose a unified structure that can represent any global tone-mapping algorithm with an array of just 256 coefficients. These coefficients extracted offline for every HDR image or video frame can be used to convert them to LDR in real time using linear interpolation. The produced LDR images are identical to the images produced by the original implementation of the algorithm. This unified implementation can replicate any global tone-mapping function and requires very low and fixed execution time, which is independent of algorithm and type of content and depends only on image size. Experimental studies are presented to show the accuracy and time efficiency of the proposed implementation.</p> </abstract>
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Khan, Muhammad Usman, Imran Mehmood, Ming Ronnier Luo, and Muhammad Farhan Mughal. "No-Reference Image Quality Metric for Tone-Mapped Images." Color and Imaging Conference 2019, no. 1 (October 21, 2019): 252–55. http://dx.doi.org/10.2352/issn.2169-2629.2019.27.45.
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Tone-mapping operators transform high dynamic range (HDR) images into displayable low dynamic range (LDR) images. Image quality evaluation of these LDR images is not possible by comparison with their corresponding high dynamic range images. Hence, a no-reference image quality metric for tone-mapped LDR images is proposed based on the fitting to the present psychophysical results including different visual image quality attributes. Ten images, including HDR natural scenes, were tonemapped using six TMOs. They were used in the assessment and visual attributes were determined to predict the quality of these images. The visual attributes (brightness and Naturalness) were modeled using parameters derived from CAM16-UCS. Results showed that the quality prediction of the model had a reasonable degree of accuracy.
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Jiang, Xianwu, Qingyi Gu, Tadayoshi Aoyama, Takeshi Takaki, and Idaku Ishii. "A High-Speed Vision System with Multithread Automatic Exposure Control for High-Dynamic-Range Imaging." Journal of Robotics and Mechatronics 30, no. 1 (February 20, 2018): 117–27. http://dx.doi.org/10.20965/jrm.2018.p0117.
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In this study, we develop a real-time high-frame-rate vision system with frame-by-frame automatic exposure (AE) control that can simultaneously synthesize multiple images with different exposure times into a high-dynamic-range (HDR) image for scenarios with dynamic change in illumination. By accelerating the video capture and processing for time-division multithread AE control at the millisecond level, the proposed system can virtually function as multiple AE cameras with different exposure times. This system can capture color HDR images of 512 × 512 pixels in real time at 500 fps by synthesizing four 8-bit color images with different exposure times at consecutive frames, captured at an interval of 2 ms, with pixel-level parallel processing accelerated by a GPU (Graphic Processing Unit) board. Several experimental results for scenarios with a large change in illumination are demonstrated to confirm the performance of the proposed system for real-time HDR imaging.
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Khan, Muhammad Murtaza. "High Dynamic Range Image Deghosting Using Spectral Angle Mapper." Computers 8, no. 1 (February 9, 2019): 15. http://dx.doi.org/10.3390/computers8010015.
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The generation of high dynamic range (HDR) images in the presence of moving objects results in the appearance of blurred objects. These blurred objects are called ghosts. Over the past decade, numerous deghosting techniques have been proposed for removing blurred objects from HDR images. These methods may try to identify moving objects and maximize dynamic range locally or may focus on removing moving objects and displaying static objects while enhancing the dynamic range. The resultant image may suffer from broken/incomplete objects or noise, depending upon the type of methodology selected. Generally, deghosting methods are computationally intensive; however, a simple deghosting method may provide sufficiently acceptable results while being computationally inexpensive. Inspired by this idea, a simple deghosting method based on the spectral angle mapper (SAM) measure is proposed. The advantage of using SAM is that it is intensity independent and focuses only on identifying the spectral—i.e., color—similarity between two images. The proposed method focuses on removing moving objects while enhancing the dynamic range of static objects. The subjective and objective results demonstrate the effectiveness of the proposed method.
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Al_airaji, Roa'a M., Ibtisam A. Aljazaery, Suha Kamal Al_Dulaimi, and Haider TH Salim Alrikabi. "Generation of high dynamic range for enhancing the panorama environment." Bulletin of Electrical Engineering and Informatics 10, no. 1 (February 1, 2021): 138–47. http://dx.doi.org/10.11591/eei.v10i1.2362.
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This paper presents a methodology for enhancement of panorama images environment by calculating high dynamic range. Panorama is constructing by merge of several photographs that are capturing by traditional cameras at different exposure times. Traditional cameras usually have much lower dynamic range compared to the high dynamic range in the real panorama environment, where the images are captured with traditional cameras will have regions that are too bright or too dark. A more details will be visible in bright regions with a lower exposure time and more details will be visible in dark regions with a higher exposure time. Since the details in both bright and dark regions cannot preserve in the images that are creating using traditional cameras, the proposed system have to calculate one using the images that traditional camera can actually produce. The proposed systems start by get LDR panorama image from multiple LDR images using SIFT features technology and then convert this LDR panorama image to the HDR panorama image using inverted local patterns. The results in this paper explained that the HDR panorama images that resulting from the proposed method is more realistic image and appears as it is a real panorama environment.
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Woo, Taeseong, Hye Yun Kim, Su Yeon Kim, Byungjae Hwang, Cheolwoo Ahn, Seok-Kyu Kwon, Jae-Ick Kim, and Jung-Hoon Park. "High-throughput high-dynamic range imaging by spatiotemporally structured illumination." APL Photonics 7, no. 10 (October 1, 2022): 106106. http://dx.doi.org/10.1063/5.0099780.
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Recent advances in biochemistry and optics have enabled observation of the faintest signals from even single molecules. However, although biological samples can have varying degrees of fluorescence expression ranging from a single to thousands of fluorescent molecules in an observation volume, the detection range is fundamentally limited by the dynamic range (DR) of current detectors. In other words, for many biological systems where faint and strong signal sources coexist, traditional imaging methods make a compromise and end up choosing a limited target signal range to be quantitatively measured while other signal levels are either lost beneath the background noise or saturated. The DR can be extended by taking multiple images with varying exposures, which, however, severely restricts data throughput. To overcome this limitation, we introduce structured illumination high dynamic range (SI-HDR) imaging, which enables real-time HDR imaging with a single measurement. We demonstrate the wide and easy applicability of the method by realizing various applications, such as high throughput gigapixel imaging of mouse brain slices, quantitative analysis of neuronal mitochondria structures, and fast 3D volumetric HDR imaging.
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Kiran, Bagadi Ravi, Vatsavayi Valli Kumari, and KVSVN Raju. "Model for High Dynamic Range Imaging System Using Hybrid Feature Based Exposure Fusion." Journal of Intelligent Systems 30, no. 1 (October 13, 2020): 346–60. http://dx.doi.org/10.1515/jisys-2018-0412.
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Abstract The luminous value is high for many natural scenes, which causes loss of information and occurs in dark images. The High Dynamic Range (HDR) technique captures the same objects or scene for multiple times in different exposure and produces the images with proper illumination. This technique is used in the various applications such as medical imaging and observing the skylight, etc. HDR imaging techniques usually have the issue of lower efficiency due to capturing of multiple photos. In this paper, an efficient method is proposed for HDR imaging technique to achieve better performance and lower noise. The Luminance-Chrominance-Gradient High Dynamic Range (LCGHDR) method is proposed to obtain the proper luminous value of images. The same scenario is captured at different exposure are processed by the proposed method. Based on these feature values extracted from the different images and exposure fusion technique was developed that helps for the proper imaging. This experiment was evaluated and analyzed by comparing with the other methods, which showed the efficiency of the proposed method. This method needs only 124.594 seconds for the computation, while existing method need 139.869 seconds for the same number of images.
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Liang, Lei, Ning Mei Yu, and Jian Wei Li. "Photo Response Non-Uniformity Correction of High Dynamic Range Video System." Applied Mechanics and Materials 263-266 (December 2012): 2524–29. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2524.
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In order to solve the problem of the photo response non-uniformity (PRNU) for High Dynamic Range (HDR) video system, this paper discusses the causes of the problem and the common solution, and then proposed a new non-uniformity correction approach based on the reference source for HDR video system. This approach use high-pass filter to calibration image in Frequency domain to get the correction matrix of one level light conditions at first. And then performs an inverse Fourier transform to the spatial domain. Finally calculate the difference between the correction matrix and the target image to obtain the corrected results. The experimental result indicates that the approach has good effect on dealing with non-uniformity of the HDR images and the definition has been greatly improved.
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Lee, Min Jung, Chi-hyoung Rhee, and Chang Ha Lee. "HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN." Applied Sciences 12, no. 5 (February 24, 2022): 2370. http://dx.doi.org/10.3390/app12052370.
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Most photographs are low dynamic range (LDR) images that might not perfectly describe the scene as perceived by humans due to the difference in dynamic ranges between photography and natural scenes. High dynamic range (HDR) images have been used widely to depict the natural scene as accurately as possible. Even though HDR images can be generated by an exposure bracketing method or HDR-supported cameras, most photos are still taken as LDR due to annoyance. In this paper, we propose a method that can produce an HDR image from a single arbitrary exposure LDR image. The proposed method, HSVNet, is a deep learning architecture using a Convolutional Neural Networks (CNN) based U-net. Our model uses the HSV color space that enables the network to identify saturated regions and adaptively focus on crucial components. We generated a paired LDR-HDR image dataset of diverse scenes including under/oversaturated regions for training and testing. We also show the effectiveness of our method through experiments, compared to existing methods.
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Li, Yumei, Ningfang Liao, Wenmin Wu, Chenyang Deng, Yasheng Li, Qiumei Fan, and Chuanjie Liu. "Tone Mapping Operator for High Dynamic Range Images Based on Modified iCAM06." Sensors 23, no. 5 (February 24, 2023): 2516. http://dx.doi.org/10.3390/s23052516.
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This study attempted to solve the problem of conventional standard display devices encountering difficulties in displaying high dynamic range (HDR) images by proposing a modified tone-mapping operator (TMO) based on the image color appearance model (iCAM06). The proposed model, called iCAM06-m, combined iCAM06 and a multi-scale enhancement algorithm to correct the chroma of images by compensating for saturation and hue drift. Subsequently, a subjective evaluation experiment was conducted to assess iCAM06-m considering other three TMOs by rating the tone mapped images. Finally, the objective and subjective evaluation results were compared and analyzed. The results confirmed the better performance of the proposed iCAM06-m. Furthermore, the chroma compensation effectively alleviated the problem of saturation reduction and hue drift in iCAM06 for HDR image tone-mapping. In addition, the introduction of multi-scale decomposition enhanced the image details and sharpness. Thus, the proposed algorithm can overcome the shortcomings of other algorithms and is a good candidate for a general purpose TMO.
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Son, Vu Hong. "A high dynamic range imaging algorithm: implementation and evaluation." Science and Technology Development Journal 22, no. 3 (August 7, 2019): 293–307. http://dx.doi.org/10.32508/stdj.v22i3.871.
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Camera specifications have become smaller and smaller, accompanied with great strides in technology and thinner product demands, which have led to some challenges and problems. One of those problems is that the image quality is reduced at the same time. The decrement of radius lens is also a cause leading to the sensor not absorbing a sufficient amount of light, resulting in captured images which include more noise. Moreover, current image sensors cannot preserve whole dynamic range in the real world. This paper proposes a Histogram Based Exposure Time Selection (HBETS) method to automatically adjust the proper exposure time of each lens for different scenes. In order to guarantee at least two valid reference values for High Dynamic Range (HDR) image processing, we adopt the proposed weighting function that restrains random distributed noise caused by micro-lens and produces a high quality HDR image. In addition, an integrated tone mapping methodology, which keeps all details in bright and dark parts when compressing the HDR image to Low Dynamic Range (LDR) image for display on monitors, is also proposed. Eventually, we implement the entire system on Adlink MXC-6300 platform that can reach 10 fps to demonstrate the feasibility of the proposed technology.
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Kim, Junghee, Siyeong Lee, and Suk-Ju Kang. "End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure Images." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1780–88. http://dx.doi.org/10.1609/aaai.v35i2.16272.
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Recently, high dynamic range (HDR) image reconstruction based on the multiple exposure stack from a given single exposure utilizes a deep learning framework to generate high-quality HDR images. These conventional networks focus on the exposure transfer task to reconstruct the multi-exposure stack. Therefore, they often fail to fuse the multi-exposure stack into a perceptually pleasant HDR image as the inversion artifacts occur. We tackle the problem in stack reconstruction-based methods by proposing a novel framework with a fully differentiable high dynamic range imaging (HDRI) process. By explicitly using the loss, which compares the network's output with the ground truth HDR image, our framework enables a neural network that generates the multiple exposure stack for HDRI to train stably. In other words, our differentiable HDR synthesis layer helps the deep neural network to train to create multi-exposure stacks while reflecting the precise correlations between multi-exposure images in the HDRI process. In addition, our network uses the image decomposition and the recursive process to facilitate the exposure transfer task and to adaptively respond to recursion frequency. The experimental results show that the proposed network outperforms the state-of-the-art quantitative and qualitative results in terms of both the exposure transfer tasks and the whole HDRI process.
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Pinho, Tatiana M., João Paulo Coelho, Josenalde Oliveira, and José Boaventura-Cunha. "Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting." Journal of Sensors 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7321950.
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Precision agriculture is gaining an increasing interest in the current farming paradigm. This new production concept relies on the use of information technology (IT) to provide a control and supervising structure that can lead to better management policies. In this framework, imaging techniques that provide visual information over the farming area play an important role in production status monitoring. As such, accurate representation of the gathered production images is a major concern, especially if those images are used in detection and classification tasks. Real scenes, observed in natural environment, present high dynamic ranges that cannot be represented by the common LDR (Low Dynamic Range) devices. However, this issue can be handled by High Dynamic Range (HDR) images since they have the ability to store luminance information similarly to the human visual system. In order to prove their advantage in image processing, a comparative analysis between LDR and HDR images, for fruits detection and counting, was carried out. The obtained results show that the use of HDR images improves the detection performance to more than 30% when compared to LDR.
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Dong, Xuan, Xiaoyan Hu, Weixin Li, Xiaojie Wang, and Yunhong Wang. "MIEHDR CNN: Main Image Enhancement based Ghost-Free High Dynamic Range Imaging using Dual-Lens Systems." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1264–72. http://dx.doi.org/10.1609/aaai.v35i2.16214.
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We study the High Dynamic Range (HDR) imaging problem using two Low Dynamic Range (LDR) images that are shot from dual-lens systems in a single shot time with different exposures. In most of the related HDR imaging methods, the problem is usually solved by Multiple Images Merging, i.e. the final HDR image is fused from pixels of all the input LDR images. However, ghost artifacts can be hardly avoided using this strategy. Instead of directly merging the multiple LDR inputs, we use an indirect way which enhances the main image, i.e. the short exposure image IS, using the long exposure image IL serving as guidance. In detail, we propose a new model, named MIEHDR CNN model, which consists of three subnets, i.e. Soft Warp CNN, 3D Guided Denoising CNN and Fusion CNN. The Soft Warp CNN aligns IL to get the aligned result ILA using the soft exposed result of IS as reference. The 3D Guided Denoising CNN denoises the soft exposed result of IS using ILA as guidance, whose result are fed into the Fusion CNN with IS to get the HDR result. The MIEHDR CNN model is implemented by MindSpore and experimental results show that we can outperform related methods largely and avoid ghost artifacts.
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Jung, Sung-Woon, Hyuk-Ju Kwon, and Sung-Hak Lee. "Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset." Applied Sciences 11, no. 16 (August 23, 2021): 7754. http://dx.doi.org/10.3390/app11167754.
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High-dynamic-range (HDR) imaging is a digital image processing technique that enhances an image’s visibility by modifying its color and contrast ranges. Generative adversarial networks (GANs) have proven to be potent deep learning models for HDR imaging. However, obtaining a sufficient volume of training image pairs is difficult. This problem has been solved using CycleGAN, but the result of the use of CycleGAN for converting a low-dynamic-range (LDR) image to an HDR image exhibits problematic color distortion, and the intensity of the output image only slightly changes. Therefore, we propose a GAN training optimization model for converting LDR images into HDR images. First, a gamma shift method is proposed for training the GAN model with an extended luminance range. Next, a weighted loss map trains the GAN model for tone compression in the local area of images. Then, a regional fusion training model is used to balance the training method with the regional weight map and the restoring speed of local tone training. Finally, because the generated module tends to show a good performance in bright images, mean gamma tuning is used to evaluate image luminance channels, which are then fed into modules. Tests are conducted on foggy, dark surrounding, bright surrounding, and high-contrast images. The proposed model outperforms conventional models in a comparison test. The proposed model complements the performance of an object detection model even in a real night environment. The model can be used in commercial closed-circuit television surveillance systems and the security industry.
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Lan, Chi-Feng, Chung-Ming Wang, and Woei Lin. "XtoE: A Novel Constructive and Camouflaged Adaptive Data Hiding and Image Encryption Scheme for High Dynamic Range Images." Applied Sciences 12, no. 24 (December 14, 2022): 12856. http://dx.doi.org/10.3390/app122412856.
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High dynamic range (HDR) image data hiding and encryption has attracted much interest in recent years due the benefits of providing high quality realistic images and versatile applications, such as copyright protection, data integrity, and covert communication. In this paper, we propose a novel constructive and camouflaged adaptive data hiding and image encryption scheme for HDR images. Our algorithm disguises hidden messages when converting an original OpenEXR format to the RGBE encoding, which contains the Red, Green, and Blue color channels and an exponent E channel. During the conversion process, we determine an optimal base for each pixel by considering the user’s demands and the exponent E channel information to achieve adaptive message concealment. To prevent inappropriate access to the stego image, we perform the bit-level permutation and confusion using a 2D Sine Logistic modulation map with hyperchaotic behavior and a random permutation scheme with the time complexity of To the best of our knowledge, our algorithm is the first in HDR data hiding literature able to predict the image distortion and satisfy a user’s request for the embedding capacity. Our algorithm offers 18% to 32% larger embedding rate than that provided by the current state-of-the-art works without degrading the quality of the stego image. Experimental results confirm that our scheme provides high security superior to the competitors.
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Tao, Xingyu, Weiqi Jin, Jianguo Yang, Shuo Li, Binghua Su, and Minghe Wang. "Multi-Integration Time Adaptive Selection Method for Superframe High-Dynamic-Range Infrared Imaging Based on Grayscale Information." Sensors 22, no. 11 (June 2, 2022): 4258. http://dx.doi.org/10.3390/s22114258.
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With the development of superframe high-dynamic-range infrared imaging technology that extends the dynamic range of thermal imaging systems, a key issue that has arisen is how to choose different integration times to obtain an HDR fusion image that contains more information. This paper proposes a multi-integration time adaptive method, in order to address the lack of objective evaluation methods for the selection of superframe infrared images, consisting of the following steps: image evaluation indicators are used to obtain the best global exposure image (the optimal integration time); images are segmented by region-growing point to obtain the ambient/high-temperature regions, selecting the local optimum images with grayscale closest to the medium grayscale of the IR imaging system for the two respective regions (lowest and highest integration time); finally, the three images above are fused and enhanced to achieve HDR infrared imaging. By comparing this method with some existing integration time selection methods and applying the proposed method to some typical fusion methods, via subjective and objective evaluation, the proposed method is shown to have obvious advantages over existing algorithms, and it can optimally select the images from different integration time series images to form the best combination that contains full image information, expanding the dynamic range of the IR imaging system.
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Liu, Yan, Bingxue Lv, Wei Huang, Baohua Jin, and Canlin Li. "Anti-Shake HDR Imaging Using RAW Image Data." Information 11, no. 4 (April 16, 2020): 213. http://dx.doi.org/10.3390/info11040213.
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Camera shaking and object movement can cause the output images to suffer from blurring, noise, and other artifacts, leading to poor image quality and low dynamic range. Raw images contain minimally processed data from the image sensor compared with JPEG images. In this paper, an anti-shake high-dynamic-range imaging method is presented. This method is more robust to camera motion than previous techniques. An algorithm based on information entropy is employed to choose a reference image from the raw image sequence. To further improve the robustness of the proposed method, the Oriented FAST and Rotated BRIEF (ORB) algorithm is adopted to register the inputs, and a simple Laplacian pyramid fusion method is implanted to generate the high-dynamic-range image. Additionally, a large dataset with 435 various exposure image sequences is collected, which includes the corresponding JPEG image sequences to test the effectiveness of the proposed method. The experimental results illustrate that the proposed method achieves better performance in terms of anti-shake ability and preserves more details for real scene images than traditional algorithms. Furthermore, the proposed method is suitable for extreme-exposure image pairs, which can be applied to binocular vision systems to acquire high-quality real scene images, and has a lower algorithm complexity than deep learning-based fusion methods.
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Pizág, Bertalan, and Balázs Vince Nagy. "Extrapolation of Saturated Diffraction Spikes in Photographs Containing Light Sources." Periodica Polytechnica Mechanical Engineering 64, no. 3 (June 29, 2020): 233–39. http://dx.doi.org/10.3311/ppme.16044.
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The observed luminance of most light sources is many orders of magnitude higher than the luminance of surrounding objects and the background. With the dynamic range of single photographs limited to 8 to 10 bits, both dark and bright values are radically coerced to the limits. This problem is usually circumvented with the use of High-Dynamic-Range (HDR) imaging, assembling multiple photographs of the same scene made at different exposures. But in some situations, or due to equipment limitations, HDR imaging might not be possible. This research is aimed at the extrapolation of luminance peaks within oversaturated Low-Dynamic-Range (LDR) images. The proposed method of extrapolation relies on the identification and analysis of Fraunhofer diffraction patterns created by the aperture. The algorithm is tested on a set of HDR images containing one or two lamps. These images are converted to LDR at a custom saturation cap, then extrapolated to restore the original peaks with relative success.
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Yoon, Howoon, S. M. Nadim Uddin, and Yong Ju Jung. "Multi-Scale Attention-Guided Non-Local Network for HDR Image Reconstruction." Sensors 22, no. 18 (September 17, 2022): 7044. http://dx.doi.org/10.3390/s22187044.
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High-dynamic-range (HDR) image reconstruction methods are designed to fuse multiple Low-dynamic-range (LDR) images captured with different exposure values into a single HDR image. Recent CNN-based methods mostly perform local attention- or alignment-based fusion of multiple LDR images to create HDR contents. Depending on a single attention mechanism or alignment causes failure in compensating ghosting artifacts, which can arise in the synthesized HDR images due to the motion of objects or camera movement across different LDR image inputs. In this study, we propose a multi-scale attention-guided non-local network called MSANLnet for efficient HDR image reconstruction. To mitigate the ghosting artifacts, the proposed MSANLnet performs implicit alignment of LDR image features with multi-scale spatial attention modules and then reconstructs pixel intensity values using long-range dependencies through non-local means-based fusion. These modules adaptively select useful information that is not damaged by an object’s movement or unfavorable lighting conditions for image pixel fusion. Quantitative evaluations against several current state-of-the-art methods show that the proposed approach achieves higher performance than the existing methods. Moreover, comparative visual results show the effectiveness of the proposed method in restoring saturated information from original input images and mitigating ghosting artifacts caused by large movement of objects. Ablation studies show the effectiveness of the proposed method, architectural choices, and modules for efficient HDR reconstruction.
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Riza, Nabeel A., and Nazim Ashraf. "Calibration Empowered Minimalistic Multi-Exposure Image Processing Technique for Camera Linear Dynamic Range Extension." Electronic Imaging 2020, no. 7 (January 26, 2020): 213–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.7.iss-213.
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Proposed for the first time is a novel calibration empowered minimalistic multi-exposure image processing technique using measured sensor pixel voltage output and exposure time factor limits for robust camera linear dynamic range extension. The technique exploits the best linear response region of an overall nonlinear response image sensor to robustly recover via minimal count multi-exposure image fusion, the true and precise scaled High Dynamic Range (HDR) irradiance map. CMOS sensor-based experiments using a measured Low Dynamic Range (LDR) 44 dB linear region for the technique with a minimum of 2 multi-exposure images provides robust recovery of 78 dB HDR low contrast highly calibrated test targets.
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Yang, Bin, Yan Chang Wang, Pei Hong Li, and Ji Lin Liu. "HDR CCD Image Sensor System through Double-A/D Convertors." Applied Mechanics and Materials 66-68 (July 2011): 2241–47. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.2241.
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In this paper we present a CCD image sensor system with high dynamic range. This feature is achieved through double analog-to-digital convertors’(ADC) architecture and field programmable gate arrays(FPGA). By doing so, the system outputs high dynamic range images in real time. The proposed scheme is a low-cost solution in the sense that it can be built on top of any traditional sensor system with minor modifications on the system-level analog-to-digital module. Through the post-layout sensor system we show the experimental images to demonstrate the effectiveness.
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Kwon, Hyuk-Ju, and Sung-Hak Lee. "Contrast Sensitivity Based Multiscale Base–Detail Separation for Enhanced HDR Imaging." Applied Sciences 10, no. 7 (April 6, 2020): 2513. http://dx.doi.org/10.3390/app10072513.
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High dynamic range (HDR) imaging is used to represent scenes with a greater dynamic range of luminance on a standard dynamic range display. Usually, HDR images are synthesized through base–detail separations. The base layer is used for tone compression and the detail layer is used for detail preservation. The representative detail-preserved algorithm iCAM06 has a tendency to reduce the sharpness of dim surround images, because of the fixed edge-stopping function of the fast-bilateral filter (FBF). This paper proposes a novel base–detail separation and detail compensation technique using the contrast sensitivity function (CSF) in the segmented frequency domain. Experimental results show that the proposed rendering method has better sharpness features and image quality than previous methods correlated by the human visual system.
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Mehmood, Imran, Muhammad Usman Khan, Ming Ronnier Luo, and Muhammad Farhan Mughal. "Tone Mapping Operators Evaluation Based on High Quality Reference Images." Color and Imaging Conference 2019, no. 1 (October 21, 2019): 268–72. http://dx.doi.org/10.2352/issn.2169-2629.2019.27.48.
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High Dynamic Range (HDR) imaging applications have been commonly placed recently. Several tone mapping operators (TMOs) have been developed which project the HDR radiance range to that of a display. Currently, there is no agreement on a technique for evaluation of tone mapping operators. The goal of this study is to establish a method based on reference images to evaluate the TMOs. Two psychophysical experiments were carried out for the evaluation of tone mapping operators. In the first experiment, a set of high quality images were generated to possess right extents of image features including contrast, colourfulness and sharpness. These images were further used in the second experiment as reference images to evaluate different TMOs. It was found Reinhard's photographic reproduction based on local TMO gave an overall better performance. CIELAB(2:1) and S- CIELAB metrics were also used to judge colour image quality of the same TMOs. It was found that both metrics agreed well with the visual results.
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Gunawan, Irwan Prasetya, Ocarina Cloramidina, Salmaa Badriatu Syafa’ah, Rizcy Hafivah Febriani, Guson Prasamuarso Kuntarto, and Berkah Iman Santoso. "A review on high dynamic range (HDR) image quality assessment." International Journal on Smart Sensing and Intelligent Systems 14, no. 1 (2021): 1–17. http://dx.doi.org/10.21307/ijssis-2021-010.
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Merianos, Ioannis, and Nikolaos Mitianoudis. "Multiple-Exposure Image Fusion for HDR Image Synthesis Using Learned Analysis Transformations." Journal of Imaging 5, no. 3 (February 26, 2019): 32. http://dx.doi.org/10.3390/jimaging5030032.
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Modern imaging applications have increased the demand for High-Definition Range (HDR) imaging. Nonetheless, HDR imaging is not easily available with low-cost imaging sensors, since their dynamic range is rather limited. A viable solution to HDR imaging via low-cost imaging sensors is the synthesis of multiple-exposure images. A low-cost sensor can capture the observed scene at multiple-exposure settings and an image-fusion algorithm can combine all these images to form an increased dynamic range image. In this work, two image-fusion methods are combined to tackle multiple-exposure fusion. The luminance channel is fused using the Mitianoudis and Stathaki (2008) method, while the color channels are combined using the method proposed by Mertens et al. (2007). The proposed fusion algorithm performs well without halo artifacts that exist in other state-of-the-art methods. This paper is an extension version of a conference, with more analysis on the derived method and more experimental results that confirm the validity of the method.
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Shaikh, Muhammad Saad, Keyvan Jaferzadeh, and Benny Thörnberg. "Extending Effective Dynamic Range of Hyperspectral Line Cameras for Short Wave Infrared Imaging." Sensors 22, no. 5 (February 25, 2022): 1817. http://dx.doi.org/10.3390/s22051817.
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In this work, a multi-exposure method is proposed to increase the dynamic range (DR) of hyperspectral imaging using an InGaAs-based short-wave infrared (SWIR) hyperspectral line camera. Spectral signatures of materials were captured for scenarios in which the DR of a scene was greater than the DR of a line camera. To demonstrate the problem and test the proposed multi-exposure method, plastic detection in food waste and polymer sorting were chosen as the test application cases. The DR of the hyperspectral camera and the test samples were calculated experimentally. A multi-exposure method is proposed to create high-dynamic-range (HDR) images of food waste and plastic samples. Using the proposed method, the DR of SWIR imaging was increased from 43 dB to 73 dB, with the lowest allowable signal-to-noise ratio (SNR) set to 20 dB. Principal Component Analysis (PCA) was performed on both HDR and non-HDR image data from each test case to prepare the training and testing data sets. Finally, two support vector machine (SVM) classifiers were trained for each test case to compare the classification performance of the proposed multi-exposure HDR method against the single-exposure non-HDR method. The HDR method was found to outperform the non-HDR method in both test cases, with the classification accuracies of 98% and 90% respectively, for the food waste classification, and with 95% and 35% for the polymer classification.
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Kim, Minjung, Maryam Azimi, and Rafał K. Mantiuk. "Perceptually motivated model for predicting banding artefacts in high-dynamic range images." Color and Imaging Conference 2020, no. 28 (November 4, 2020): 42–48. http://dx.doi.org/10.2352/issn.2169-2629.2020.28.8.
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Banding is a type of quantisation artefact that appears when a low-texture region of an image is coded with insufficient bitdepth. Banding artefacts are well-studied for standard dynamic range (SDR), but are not well-understood for high dynamic range (HDR). To address this issue, we conducted a psychophysical experiment to characterise how well human observers see banding artefacts across a wide range of luminances (0.1 cd/m2–10,000 cd/m2). The stimuli were gradients modulated along three colour directions: black-white, red-green, and yellow-violet. The visibility threshold for banding artefacts was the highest at 0.1 cd/m2, decreased with increasing luminance up to 100 cd/m2, then remained at the same level up to 10,000 cd/m2. We used the results to develop and validate a model of banding artefact detection. The model relies on the contrast sensitivity function (CSF) of the visual system, and hence, predicts the visibility of banding artefacts in a perceptually accurate way.
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Rousselot, Maxime, Olivier Meur, Rémi Cozot, and Xavier Ducloux. "Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces." Journal of Imaging 5, no. 1 (January 14, 2019): 18. http://dx.doi.org/10.3390/jimaging5010018.
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High Dynamic Range (HDR) and Wide Color Gamut (WCG) screens are able to render brighter and darker pixels with more vivid colors than ever. To assess the quality of images and videos displayed on these screens, new quality assessment metrics adapted to this new content are required. Because most SDR metrics assume that the representation of images is perceptually uniform, we study the impact of three uniform color spaces developed specifically for HDR and WCG images, namely, I C t C p , J z a z b z and H D R - L a b on 12 SDR quality assessment metrics. Moreover, as the existing databases of images annotated with subjective scores are using a standard gamut, two new HDR databases using WCG are proposed. Results show that MS-SSIM and FSIM are among the most reliable metrics. This study also highlights the fact that the diffuse white of HDR images plays an important role when adapting SDR metrics for HDR content. Moreover, the adapted SDR metrics does not perform well to predict the impact of chrominance distortions.
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Urquhart, B., B. Kurtz, E. Dahlin, M. Ghonima, J. E. Shields, and J. Kleissl. "Development of a sky imaging system for short-term solar power forecasting." Atmospheric Measurement Techniques Discussions 7, no. 5 (May 19, 2014): 4859–907. http://dx.doi.org/10.5194/amtd-7-4859-2014.
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Abstract. To facilitate the development of solar power forecasting algorithms based on ground-based visible wavelength remote sensing, we have developed a high dynamic range (HDR) camera system capable of providing hemispherical sky imagery from the circumsolar region to the horizon at a high spatial, temporal, and radiometric resolution. The University of California, San Diego Sky Imager (USI) captures multispectral, 16 bit, HDR images as fast as every 1.3 s. This article discusses the system design and operation in detail, provides a characterization of the system dark response and photoresponse linearity, and presents a method to evaluate noise in high dynamic range imagery. The system is shown to have radiometrically linear response to within 5% in a designated operating region of the sensor. Noise for HDR imagery is shown to be very close to the fundamental shot noise limit. The complication of directly imaging the sun and the impact on solar power forecasting is also discussed. The USI has performed reliably in a hot, dry environment, a tropical coastal location, several temperate coastal locations, and in the great plains of the United States.
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Leng, Xue, and Jin Hua Yang. "Light Illumination Information Extraction Method of High Dynamic Range Image." Advanced Materials Research 756-759 (September 2013): 952–55. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.952.
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In this Paper we Investigated Access Technology of the Light Illumination Information in Real Scene. by Analyzing the Conventional Method, we Proposed a Method to Access Light Illumination Information of High Dynamic Range Image. in the Method, the Image Information Obtained by Camera was Converted to the Panoramic Image by Using Image Mosaic Technology and then Converted to the High Dynamic Range Image by the Software HDR.
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Xiao, Yifan, Peter Veelaert, and Wilfried Philips. "Deep HDR Deghosting by Motion-Attention Fusion Network." Sensors 22, no. 20 (October 16, 2022): 7853. http://dx.doi.org/10.3390/s22207853.
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Multi-exposure image fusion (MEF) methods for high dynamic range (HDR) imaging suffer from ghosting artifacts when dealing with moving objects in dynamic scenes. The state-of-the-art methods use optical flow to align low dynamic range (LDR) images before merging, introducing distortion into the aligned LDR images from inaccurate motion estimation due to large motion and occlusion. In place of pre-alignment, attention-based methods calculate the correlation between the reference LDR image and non-reference LDR images, thus excluding misaligned regions in LDR images. Nevertheless, they also exclude the saturated details at the same time. Taking advantage of both the alignment and attention-based methods, we propose an efficient Deep HDR Deghosting Fusion Network (DDFNet) guided by optical flow and image correlation attentions. Specifically, the DDFNet estimates the optical flow of the LDR images by a motion estimation module and encodes that optical flow as a flow feature. Additionally, it extracts correlation features between the reference LDR and other non-reference LDR images. The optical flow and correlation features are employed to adaptably combine information from LDR inputs in an attention-based fusion module. Following the merging of features, a decoder composed of Dense Networks reconstructs the HDR image without ghosting. Experimental results indicate that the proposed DDFNet achieves state-of-the-art image fusion performance on different public datasets.
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Barazzetti, Luigi, Fabio Remondino, and Marco Scaioni. "Sequential Homography-Based Alignment for HDR Image Generation." Advanced Materials Research 452-453 (January 2012): 1025–29. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.1025.
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Tripoding the camera is a standard solution to acquire aligned images useful for High Dynamic Range photography. On the other hand, the chance to use a hand-held digital camera is surely more practical and attractive for photographers. In this paper we propose a registration algorithm that recovers the alignment of several bracketed images using a progressive combination of homographies estimated from a set of image correspondences.
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Jia, Yuan, and Wenting Zhang. "Efficient and Adaptive Tone Mapping Algorithm Based on Guided Image Filter." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 04 (August 1, 2019): 2054012. http://dx.doi.org/10.1142/s0218001420540129.
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The recognition rate of computer vision algorithms is highly dependent on the image quality. To enhance the visual quality of the images captured under high-dynamic range (HDR) scenes, we propose an efficient and adaptive tone mapping algorithm based on guided image filter (GIF). The HDR image is compressed adaptively according to its average luminance. Then we decompose it into a base layer and a detail layer using the guided image filter. We improve the base layer and enhance the detail layer simultaneously, and combine the two layers to get the final low-dynamic range (LDR) image. Since the parameters are linked with image statistics, they adaptively fit to various kinds of images. The objective evaluation results on HDR image sets demonstrate the superiority of our proposed algorithm. Meanwhile, the result of our algorithm can reduce the halo artifacts and preserve more detail by subjective observation.
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Sun, Qingjiao, Huiyan Jiang, Ganzheng Zhu, Siqi Li, Shang Gong, Benqiang Yang, and Libo Zhang. "HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter." BioMed Research International 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7478219.
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Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR) image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.
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Zhu, Ming, Zhen Liu, and Dan Dan Yang. "A HDR Image Compression Algorithm Based on Non-Linear Masking." Advanced Materials Research 174 (December 2010): 123–26. http://dx.doi.org/10.4028/www.scientific.net/amr.174.123.
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High Dynamic Range Images, with the capability of reproducing the wide lightness range of natural scenes, has been widely used in many areas. But the restrictions of device capability make it necessary to implement tone compression for displaying HDR images. The major research of this paper includes: ① the non-linear masking technology was analyzed and applied to the HDR image tone compression for the first time; ② the specific workflow of the HDR image compression algorithm based on non-linear masking was proposed; ③ VDP (Visible Difference Predictor) model was used to evaluate the performance of the algorithm objectively. The result of algorithm evaluation shows: the non-linear masking can achieve a good effect of tone compression, and greatly reduce the complexity of the algorithm.
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Suma, Rossella, Georgia Stavropoulou, Elisavet K. Stathopoulou, Luc Van Gool, Andreas Georgopoulos, and Alan Chalmers. "Evaluation of the effectiveness of HDR tone-mapping operators for photogrammetric applications." Virtual Archaeology Review 7, no. 15 (November 15, 2016): 54. http://dx.doi.org/10.4995/var.2016.6319.
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<p class="VARAbstract">The ability of High Dynamic Range (HDR) imaging to capture the full range of lighting in a scene has meant that it is being increasingly used for Cultural Heritage (CH) applications. Photogrammetric techniques allow the semi-automatic production of 3D models from a sequence of images. Current photogrammetric methods are not always effective in reconstructing images under harsh lighting conditions, as significant geometric details may not have been captured accurately within under- and over-exposed regions of the image. HDR imaging offers the possibility to overcome this limitation, however the HDR images need to be tone mapped before they can be used within existing photogrammetric algorithms. In this paper we evaluate four different HDR tone-mapping operators (TMOs) that have been used to convert raw HDR images into a format suitable for state-of-the-art algorithms, and in particular keypoint detection techniques. The evaluation criteria used are the number of keypoints, the number of valid matches achieved and the repeatability rate. The comparison considers two local and two global TMOs. HDR data from four CH sites were used: Kaisariani Monastery (Greece), Asinou Church (Cyprus), Château des Baux (France) and Buonconsiglio Castle (Italy).</p>
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Marnerides, Demetris, Thomas Bashford-Rogers, and Kurt Debattista. "Deep HDR Hallucination for Inverse Tone Mapping." Sensors 21, no. 12 (June 11, 2021): 4032. http://dx.doi.org/10.3390/s21124032.
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Inverse Tone Mapping (ITM) methods attempt to reconstruct High Dynamic Range (HDR) information from Low Dynamic Range (LDR) image content. The dynamic range of well-exposed areas must be expanded and any missing information due to over/under-exposure must be recovered (hallucinated). The majority of methods focus on the former and are relatively successful, while most attempts on the latter are not of sufficient quality, even ones based on Convolutional Neural Networks (CNNs). A major factor for the reduced inpainting quality in some works is the choice of loss function. Work based on Generative Adversarial Networks (GANs) shows promising results for image synthesis and LDR inpainting, suggesting that GAN losses can improve inverse tone mapping results. This work presents a GAN-based method that hallucinates missing information from badly exposed areas in LDR images and compares its efficacy with alternative variations. The proposed method is quantitatively competitive with state-of-the-art inverse tone mapping methods, providing good dynamic range expansion for well-exposed areas and plausible hallucinations for saturated and under-exposed areas. A density-based normalisation method, targeted for HDR content, is also proposed, as well as an HDR data augmentation method targeted for HDR hallucination.
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Chen, Zhe Bo, Jin Xu, Bin Lin, Xu Xiang Ni, and Zu Kang Lu. "A HDRI Camera Based on SLM." Key Engineering Materials 364-366 (December 2007): 1237–41. http://dx.doi.org/10.4028/www.scientific.net/kem.364-366.1237.
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A high dynamic range image camera (HDRI) is a kind of camera which captures the HDRI image. HDRI means the image with high dynamic range, it can record the gray information of the object with much wider dynamic range. HDR image has great applications in many fields. The existing HDRI camera needs either the special image sensor or can capture the still object only. In this paper, a new kind of camera is proposed. In this camera, a space light modulator (SLM) is used to acquire images with different exposure, then a HDRI is made with these images. This camera can acquire dynamic image and it is cheap and easy to extend.